Generative-AI models are changing faster than fixed-function neural processors can be updated. New attention variants, quantization formats, and routing schemes appear on a quarterly basis, making hardwired accelerators quickly obsolete.
To address this, Quadric's IEEE paper describes a General-Purpose Neural Processor (GPNPU), a configurable IP block that combines scalar, vector, and matrix execution into one instruction-set architecture. It is programmable from standard C++ and Python, so developers can adapt to new model features without waiting for new silicon.
The architecture features a single instruction stream feeding a scalar unit and a 2D SIMD processing-element array. All on-chip memory, including local register files and L2, is software-managed, giving programmers explicit control over data movement. This flexibility is intended to let a single processor design track the rapid evolution of generative-AI workloads.